Effect of Interferon- <b>γ</b> on Allergic Airway Responses in Interferon- <b>γ</b> –deficient Mice
Bibliographic record
Abstract
Interferon (IFN)-gamma reduces airway responses after allergen challenge in mice. The mechanisms of this effect are not clear. These studies investigate whether IFN-gamma can reverse prolonged airway responses after allergen challenge in IFN-gamma-deficient (IFN-gammaKO) mice. Sensitized mice (IFN-gammaKO and wild-type [WT]) were challenged with ovalbumin. Airway responsiveness, eosinophils in bronchoalveolar lavage fluid, and lung lymphocyte subsets (CD4(+) and CD8(+)) were measured 24 hours and 8 weeks after challenge. In further experiments, we treated IFN-gammaKO mice with recombinant IFN-gamma starting 4 weeks after the challenge for 1 week or 4 weeks. Airway responsiveness, bronchoalveolar lavage eosinophils, and lung CD4(+) cells were increased 8 weeks after challenge in IFN-gammaKO but not WT mice. IFN-gamma treatment returned lung CD4(+) cell numbers to values obtained in unchallenged mice. One week of IFN-gamma treatment also returned airway responsiveness to baseline levels; however, 4-week treatment with IFN-gamma failed to decrease airway responsiveness below levels observed in untreated animals. This suggests that IFN-gamma plays an essential role in reversing allergen-induced airway inflammation and hyperresponsiveness and that it may have dual actions on the latter. Observations that IFN-gamma reverses airway responses, even when administered after challenge, suggests that IFN-gamma treatment could control allergic disease, including asthma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".